Abstract PR010: Evolutionary Tumor Board: Implementing dynamic personalized therapy using evolutionary theory and mathematical modeling for clinical decision support
Authors: Mark Robertson-Tessi, Joel Brown, Maria Poole, Kimberly Luddy, Andriy Marusyk, Jill Gallaher, Jeffrey West, Matthew Johnson, Heiko Enderling, Rikesh Makanji, Joaquim Farinhas, Robert Gatenby, Damon Reed, Christine Chung, Alexander Anderson
Journal: Cancer Research (2022)

Abstract The era of big data in oncology has led to the promise of precision medicine for individual patients. However, many therapy decisions continue to be made based on “one size fits most” approaches, primarily since there exist few theoretical and practical tools to deal with a patient’s data over time. In parallel with this growing interest in personalized medicine, cancer is being increasingly recognized as an eco-evolutionary system that adapts to treatments, suggesting that static therapy regimens are often doomed to eventual failure. Here, we present preliminary results from a novel pilot clinical trial (NCT04343365), the Evolutionary Tumor Board (ETB), which uses eco-evolutionary theory (based on experiments and modeling) to assist with clinical decision making for each patient. We developed an informational and computational framework for applying evolutionary therapy approaches to individual patients in a dynamic fashion, using their clinical data in real time. The framework relies on detailed data curation and imaging measurements for each patient, as well as a mathematical modeling approach that accounts for multi-lesion tumor growth, treatment-induced death, and the evolution of resistance. The models are calibrated by historical datasets of similar patients, as well as the patient’s own temporal data. We use a “Phase i trial” approach to account for prediction uncertainty and provide decision support for therapy options available to the patient at any given time point. Crucially, this is presented in a way that harmonizes with the treating oncologist’s intuition. Fifteen patients at Moffitt have been enrolled into the ETB, many of whom have proceeded through the entire process, including follow-up analysis. The ETB generated outcome predictions and therapy recommendations for each case, and subsequent follow-up predictions and recommendations. Our current results demonstrate that the ETB approach has provided both novel and useful decision support for the clinicians. At the same time, numerous opportunities for further research and development have been identified. Our efforts show that there are both challenges and opportunities in the area of personalized therapy, particularly in the context of real-time clinical care. Early results from the ETB show great promise for improving patient outcomes in cancer using mathematical modeling and evolutionary therapy. Citation Format: Mark Robertson-Tessi, Joel Brown, Maria Poole, Kimberly Luddy, Andriy Marusyk, Jill Gallaher, Jeffrey West, Matthew Johnson, Heiko Enderling, Rikesh Makanji, Joaquim Farinhas, Robert Gatenby, Damon Reed, Christine Chung, Alexander Anderson. Evolutionary Tumor Board: Implementing dynamic personalized therapy using evolutionary theory and mathematical modeling for clinical decision support [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr PR010.